{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:19:27Z","timestamp":1772788767776,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS- 1849085, CNS-1816497, IIS-1750074, IIS-2006844"],"award-info":[{"award-number":["IIS- 1849085, CNS-1816497, IIS-1750074, IIS-2006844"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512170","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:13:07Z","timestamp":1650863587000},"page":"1226-1237","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Geometric Graph Representation Learning via Maximizing Rate Reduction"],"prefix":"10.1145","author":[{"given":"Xiaotian","family":"Han","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, USA"}]},{"given":"Zhimeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, USA"}]},{"given":"Ninghao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Georgia, USA"}]},{"given":"Qingquan","family":"Song","sequence":"additional","affiliation":[{"name":"LinkedIn, USA"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}]},{"given":"Xia","family":"Hu","sequence":"additional","affiliation":[{"name":"Rice University, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In ICLR."},{"key":"e_1_3_2_1_2_1","volume-title":"ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108\u2013122","author":"Buitinck Lars","year":"2013","unstructured":"Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Ga\u00ebl Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108\u2013122."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2015974"},{"key":"e_1_3_2_1_4_1","volume-title":"Finding community structure in very large networks. Physical review E 70, 6","author":"Clauset Aaron","year":"2004","unstructured":"Aaron Clauset, Mark\u00a0EJ Newman, and Cristopher Moore. 2004. Finding community structure in very large networks. Physical review E 70, 6 (2004), 066111."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_3_2_1_6_1","unstructured":"Nicola De\u00a0Cao and Thomas Kipf. 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973(2018)."},{"key":"e_1_3_2_1_7_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS. 3844\u20133852."},{"key":"e_1_3_2_1_8_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan\u00a0E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_9_1","volume-title":"ICML. PMLR","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In ICML. PMLR, 2083\u20132092."},{"key":"e_1_3_2_1_10_1","unstructured":"Alberto Garc\u00eda-Dur\u00e1n and Mathias Niepert. 2017. Learning graph representations with embedding propagation. arXiv preprint arXiv:1710.03059(2017)."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. 249\u2013256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. 249\u2013256."},{"key":"e_1_3_2_1_12_1","volume-title":"Linear algebra","author":"Golub H","unstructured":"Gene\u00a0H Golub and Christian Reinsch. 1971. Singular value decomposition and least squares solutions. In Linear algebra. Springer, 134\u2013151."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_14_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024\u20131034."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330941"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/757584395"},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A Method for Stochastic Optimization. In ICLR.","author":"Kingma P","year":"2015","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_1_18_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016)."},{"key":"e_1_3_2_1_19_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1085"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_23_1","volume-title":"On principal angles between subspaces in Rn. Linear algebra and its applications 171","author":"Miao Jianming","year":"1992","unstructured":"Jianming Miao and Adi Ben-Israel. 1992. On principal angles between subspaces in Rn. Linear algebra and its applications 171 (1992), 81\u201398."},{"key":"e_1_3_2_1_24_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg Corrado and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546(2013)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_1_26_1","unstructured":"Andrew\u00a0Y Ng Michael\u00a0I Jordan and Yair Weiss. 2002. On spectral clustering: Analysis and an algorithm. In NeurIPS. 849\u2013856."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Complex Networks and their Applications. Springer, 229\u2013240","author":"Par\u00e9s Ferran","year":"2017","unstructured":"Ferran Par\u00e9s, Dario\u00a0Garcia Gasulla, Armand Vilalta, Jonatan Moreno, Eduard Ayguad\u00e9, Jes\u00fas Labarta, Ulises Cort\u00e9s, and Toyotaro Suzumura. 2017. Fluid communities: A competitive, scalable and diverse community detection algorithm. In International Conference on Complex Networks and their Applications. Springer, 229\u2013240."},{"key":"e_1_3_2_1_28_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library.. In NeurIPS."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"e_1_3_2_1_32_1","volume-title":"Nonlinear dimensionality reduction by locally linear embedding. science 290, 5500","author":"Roweis T","year":"2000","unstructured":"Sam\u00a0T Roweis and Lawrence\u00a0K Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. science 290, 5500 (2000), 2323\u20132326."},{"key":"e_1_3_2_1_33_1","unstructured":"Oleksandr Shchur Maximilian Mumme Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868(2018)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_35_1","volume-title":"A global geometric framework for nonlinear dimensionality reduction. science 290, 5500","author":"Tenenbaum B","year":"2000","unstructured":"Joshua\u00a0B Tenenbaum, Vin De\u00a0Silva, and John\u00a0C Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. science 290, 5500 (2000), 2319\u20132323."},{"key":"e_1_3_2_1_36_1","volume-title":"Visualizing data using t-SNE.Journal of machine learning research 9, 11","author":"Maaten Laurens Van\u00a0der","year":"2008","unstructured":"Laurens Van\u00a0der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008)."},{"key":"e_1_3_2_1_37_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_38_1","unstructured":"Petar Veli\u010dkovi\u0107 William Fedus William\u00a0L Hamilton Pietro Li\u00f2 Yoshua Bengio and R\u00a0Devon Hjelm. 2018. Deep Graph Infomax. In ICLR."},{"key":"e_1_3_2_1_39_1","volume-title":"Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1-3","author":"Wold Svante","year":"1987","unstructured":"Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1-3 (1987), 37\u201352."},{"key":"e_1_3_2_1_40_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR 6861\u20136871."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 33rd ICML.","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William\u00a0W Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. In Proceedings of the 33rd ICML."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_44_1","volume-title":"Graph contrastive learning with augmentations. NeurIPS","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NeurIPS (2020)."},{"key":"e_1_3_2_1_45_1","volume-title":"Chong You, Chaobing Song, and Yi Ma.","author":"Yu Yaodong","year":"2020","unstructured":"Yaodong Yu, Kwan Ho\u00a0Ryan Chan, Chong You, Chaobing Song, and Yi Ma. 2020. Learning diverse and discriminative representations via the principle of maximal coding rate reduction. NeurIPS 33(2020)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_47_1","volume-title":"Learning from labeled and unlabeled data with label propagation","author":"Zhu X","year":"2002","unstructured":"X Zhu and Z Ghahramani. 2002. Learning from labeled and unlabeled data with label propagation. Center for Automated Learning and Discovery, CMU: Carnegie Mellon University, USA. (2002)."},{"key":"e_1_3_2_1_48_1","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131(2020)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512170","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512170","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:14Z","timestamp":1750188674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":49,"alternative-id":["10.1145\/3485447.3512170","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512170","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}